Human identification by medical findings in a forensic anthropology context.

This article presents a series of three complex forensic cases that posed significant challenges for identifying human remains. These include a mass dam disaster, burnt human remains, and extensively decomposed human remains. Positive identification was achieved using a shadow positioning technique with imaging comparisons of medical findings. After establishing the biological profile, medical data were evaluated with digital radiography and computed tomography examinations the human remains. These aimed to replicate the original (intravitam) traits in the same angulation to examine postsurgical characteristics, as well as the anatomical, pathological, and morphological features, which were sufficient to establish a positive scientific identification. Technological advancements tend to reveal additional skeletal details, making medical data comparisons significantly more effective in the context of anthropological identification. These cases demonstrate that the possibility of identification should never be ignored, even in situations with advanced decomposition. Key points: Conventional identification methods may not always be applicable in forensic anthropology cases.The presented cases include a mass dam disaster, burnt human remains, and extensively decomposed human remains.These three cases involved successful human identification with medical findings comparisons using the shadow position technique.Identification could be established in these cases, despite challenges, such as fire damage, an incomplete body, and extensive decomposition.These cases suggest medical findings should be considered as biological identifiers rather than secondary identifiers.

Identification score for robust and secure identification using ante- and post-mortem skull CT scans.

Due to their unique anatomy, paranasal sinuses have been used for comparative identification between post-mortem CT (PMCT) and ante-mortem CT (AMCT). However, data security issues arise when transferring raw AMCT images of a suspected identity. The aim of this study was to derive and validate an identification score based on CT slices extracted from successive CTs for the identification of subjects. For derivation procedure, we included patients who underwent two successive AMCTs at ≥ 1-year interval (n = 98), and 4 radiologists individually assessed similarity of prespecified CT slices (centered on ethmoid, frontal sinus and Left Semi-Circular Canal). Predictive values were calculated for all combinations of number of readers and slices, and the optimal compromise, termed IDScore, was selected. For validation, we included PMCTs performed between 2018 and 2022 with available comparative head AMCTs (n = 27). For each PMCT, 5 comparison procedures were performed: 1 concordant (with corresponding AMCT) and 4 discordant (with randomly selected AMCTs). Two radiologists evaluated similarity of ethmoid and frontal CT slices with a score ranging from -2 to + 2. IDScore was defined as the sum of these slice scores, averaged between the two readers. In the 135 comparison procedures, IDScore using predetermined thresholds (positive identification for IDScore > + 2, negative identification for IDScore < -1) allowed a perfect discrimination between identical subjects (Sensitivity = 100%, Specificity = 100%). IDScore could be used for remote identification of a subject with no need to access to the complete raw AMCT images, hence helping to overcome ethical and regulatory issues to access AMCT of a suspected identity.Trial registration: F20220729161623 on Health Data Hub, registered on 29 July 2022.

Challenges of accreditation in forensic fields concerned with human identification: a survey of European facial examiners.

Forensic anthropology and forensic facial image identification are areas with two aspects in common: (i) the use of anthropological knowledge concerning human variation in their analyses and (ii) low numbers of accredited forensic units. While the low numbers are often explained by the uniqueness of human identification cases, given the high level of scrutiny in the forensic sciences, interest in and efforts to achieve accreditation have become increasingly prominent. Therefore, this study aimed to obtain accreditation-relevant information about the working environment in facial image comparison units by surveying facial examiners across Europe. Two surveys were distributed: One was given to participants of a European Commission-funded training course for technical assessors in facial image comparison, and the second, more comprehensive survey, was distributed to members of the European Network of Forensic Science Institutes Digital Imaging Working Group. Thirty-four responses from facial examiners from 16 countries were received. All respondents worked for a governmental organization, nine (26.5%) in accredited units, and 12 (35.3%) had worked as facial examiners for more than 11 years. More than 80% of respondents had an academic background. All examiners from accredited units reported having standard operating procedures, annual Digital Imaging Working Group proficiency testing, and using a standard methodology (compared with 72%, 92%, and 84% from nonaccredited units, respectively). The survey found that working conditions in forensic facial image identification vary among European countries. Some respondents from nonaccredited units reported that their unit had no standard operating procedures, with proficiency tests and intralaboratory validations not performed regularly, and an inconsistently used standard methodology. As these conditions are typically required for successful accreditation, a better understanding of best practice and accreditation requirements in the field is needed. Facilitating interactions between forensic practitioners and quality managers may prove beneficial for future accreditation efforts. Key points: Survey of European facial examiners focused on accreditation-relevant topics.Respondents from accredited facial image comparison units differed from those of nonaccredited units in terms of better awareness of standard operating procedures, uptake of intra- and interlaboratory testing, and the consistent use of standard methods and regular method validation.A better understanding of accreditation requirements and best practices recommendations would help to harmonize the practice of the forensic sciences related to human identification.

WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey.

In the modern world of human-computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals.

A Biometric Identification for Multi-Modal Biomedical Signals in Geriatric Care.

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time-frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.

Characterisation of identity-informative genetic markers in the Australian population with European ancestry.

Identity-informative single nucleotide polymorphisms (iiSNPs) are valuable genetic markers for human identification and kinship testing in forensic casework, especially when the quality and quantity of DNA evidence is not suitable for routine short tandem repeat (STR) profiling. This study analysed 105 buccal samples representing the Australian population with European ancestry in order to assign allele frequencies and conduct population genetic analyses for 94 iiSNPs and 20 STRs. The markers were assessed by calculating relevant forensic statistics and testing for deviations from Hardy-Weinberg and linkage equilibrium. No linkage of statistical significance was observed between any of the pair-wise combinations of the combined 114 identity-informative markers and only one STR exhibited deviation from Hardy-Weinberg equilibrium (D8S1179). The probability of matching genotypes being observed within this population was of the order of 10-23 for STRs, 10-38 for iiSNPs and 10-60 for the combined identity-informative marker panel, improving the ability to discriminate between individuals when calculating likelihood ratios in direct or indirect matching scenarios. Further, the addition of iiSNPs will facilitate identifications when suboptimal STR profiles are recovered from compromised or challenging samples and aid comparisons to genetic relatives for familial or kinship testing.

Who are they? A retrospective study of unidentified bodies in Institute of Medical-Legal Paris from 2018 to 2023.

"I still don't realize that he's dead.... I cried over it. It makes me sad to know that he was buried unaccompanied on his last trip. We were all shocked." This testimony corresponds to a family whose relative was buried in an anonymous grave 6 months after his disappearance was reported to the police. It is estimated that between 1 000 and 3 000 unidentified bodies are buried in anonymous graves in France each year. Most of these decedents have passed through the medico-legal system. However the identification of these bodies, outside the context of mass disasters, remains a complex problem. Several national and international publications have highlighted the prevalent problem of unidentified burials and the consequences for families who do not know the fate of their loved ones, specifically, whether they are alive or deceased. This 6-year retrospective study (2018-2023), covering a total of 2 324 unidentified decedents admitted to the Institute of Medical-Legal Paris (IMLP), aimed to assess the impact of the identification protocol implemented in 2017 on the number of bodies that remain unidentified (n = 164). In addition, this study aimed to establish profiles for individuals who remained unidentified with the objective of identifying the factors that hinder their identification and developing correlated methods to address these issues. The results of this study were compared with other published studies to highlight the global problem and the ongoing need for collaboration between forensic practitioners and relevant authorities. Key points: Despite great advances in human identification, unidentified decedents remain a global problem.This 6-year overview study covering a total of 2 324 unidentified bodies admitted to the IMLP provided relevant information about the unidentified decedent population and assessed the impact of a protocol established in 2017 on the rate of deceased buried without identity in Paris.The need to establish a national database in France to properly document and disseminate information on missing persons and to centralize the biological profile of unidentified bodies is key, as without antemortem information or a biometric database there can be no matching.

Human identification of single hair shaft using a mass spectrometry mRNA typing system.

Hair is one of the most common forms of forensic biological material at various crime scenes. So far, human identification cannot be effectively accomplished with a single telogen hair encountered in forensic casework due to the detection limit. Emerging studies have revealed RNA as a promising biomarker in hair shafts, while the single telogen hair could not be successfully genotyped even after being examined with the recently developed mRNA typing system. MALDI-TOF MS, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, enables sensitive and accurate measurement of DNA products. To address this problem and further develop the analysis technology of hairs, we established a mass spectrometry system for human identification based on a single hair shaft using 25 polymorphic SNPs located on 18 mRNA molecules (KRT31, RFK, KRT86, KRT35, PABPC1, KMT2D, LEMD2, TBC1D4, CTC1, PPP1R15A, RBM33, LRRC15, KRT33A, KRTAP12-2, KRT81, AHNAK, KRTAP4-8, FLG2). The forensic application of the detection system was evaluated, and all hair samples used were collected from individuals in Shanxi province. Firstly, we demonstrated that the RNA typing results of a single hair shaft were in perfect concordance with DNA typing results and confirmed the consistency between hairs from different body parts. To assess the potential influence of positions along the hair shaft, 6 cm long hair shafts from the distal end were examined by the MALDI-TOF MS system, whose genotype could be successfully detected. The system was capable of detecting aged samples stored for 390 days and could also be employed on various types of hair samples, such as white hair and permed or dyed hair. Finally, 50 unrelated individuals from Shanxi province were genotyped for the population study, and the CDP of the system in the Shanxi population is 0.998928. In this study, we established a mass spectrometry system for human identification based on a single hair shaft. We used a single hair shaft, rather than multiple hair shafts reported in our previous report, to get a full typing profile. The system sensitivity was substantially enhanced, which provided a valuable strategy for forensic practice to perform human identification using hairs.

A statistical evaluation of the sexual dimorphism of the acetabulum in an Iberian population.

Sex estimation is essential for human identification within bioarchaeological and medico-legal contexts. Amongst the sexually dimorphic skeletal elements commonly utilised for this purpose, the pelvis is usually preferred because of its direct relationship with reproduction. Furthermore, the posterior part of the innominate bone has proven to have better preservation within degraded contexts. With the aim of investigating the potential of the vertical acetabular diameter as a sex marker, 668 documented individuals from three different Iberian skeletal collections were randomly divided into training and test samples and eventually analysed using different statistical approaches. Two traditional (Discriminant Function Analysis and Logistic Regression Analysis) and four Machine learning methodologies (Support Vector Classification, Decision Tree Classification, k Nearest Neighbour Classification, and Neural Networks) were performed and compared. Amongst these statistical modalities, Machine Learning methodologies yielded better accuracy outcomes, with DTC garnering highest accuracy percentages of 83.59% and 89.85% with the sex-pooled and female samples, respectively. With males, ANN yielded highest accuracy percentage of 87.70%, when compared to other statistical approaches. Higher accuracy obtained with ML, along with its minimal statistical assumptions, warrant these approaches to be increasingly utilised for further investigations involving sex estimation and human identification. In this line, the creation of a statistical platform with easier user interface can render such robust statistical modalities accessible to researchers and practitioners, effectively maximising its practical use. Future investigations should attempt to achieve this goal, alongside examining the influence of factors such as age, on the obtained accuracy outcomes.

The radiographic diversity of dental patterns among 7219 young individuals-a contribution to disaster victim identification.

The diversity of dental patterns is a fundamental topic in disaster victim identification. The current scientific literature, however, is scarce of data regarding young individuals. This study aimed to assess the radiographic diversity of dental patterns, considering missing, unrestored, and filled teeth in young individuals. The sample consisted of 7219 panoramic radiographs of individuals between 12 and 22.9 years. The permanent teeth, except third molars, were coded as missing, unrestored, or filled and odds ratios (OR) were calculated based on sex, dental arch, and age. The sex-combined sample had 1.116 distinctive dental patterns. "All unrestored" teeth was the most common pattern (OR: 0.437) followed by the sequence of unrestored teeth except restored mandibular first molars (OR: 0.021). Females had more distinctive dental patterns than males (p < .001), while males had more unrestored teeth (p < .001). In the age category of 12-12.9 years, the OR for finding a distinctive dental pattern was 11%, while in the age category of 22-22.9 years it increased to 58%. On the other hand, the OR for "all unrestored" gradually decreased according to age (74% in the younger category, and 23% in the older age category). The distinctiveness of dental patterns among young individuals is affected by the predominance of unrestored teeth. However, registering a single filled tooth in a remaining unrestored dentition can reduce exponentially the probability of finding an identical pattern of missing, unrestored and filled teeth.

Moving from the unknown to the known: a multidisciplinary approach to the identification of skeletal remains from Sandy Point, Australia.

When human remains are inadvertently located, case-related circumstantial information is used to generate an identification hypothesis, and the preservation of the remains typically informs which identification methods may then be used to validate that hypothesis. What happens, however, when there is no contextual information to generate an identification hypothesis? This paper presents the case of a near-complete human skeleton discovered at Sandy Point in Victoria, Australia. The circumstances of the case did not facilitate an identification hypothesis, and with no hypothesis to triage the identification process, all possible identification methods were employed. Preservation of the individual meant neither a visual nor a fingerprint identification was possible, and the lack of an identification hypothesis meant there was no antemortem reference data to compare with the postmortem DNA or dental information. Consequently, in addition to historical research, novel methods, such as radiocarbon dating and genetic intelligence, were utilized to complement information provided by the forensic anthropology and odontology analyses, which ultimately resulted in the identification. This example highlights the complexity of cases of unidentified skeletal remains and emphasizes the fact that identification is a process that necessarily requires a multidisciplinary and collaborative approach. Key points: Human skeletal remains were recovered from Sandy Point, Victoria.The absence of circumstantial information and the poor preservation (i.e. skeletonized) of the remains precluded the formation of an identification hypothesis, rendering the identification process complex.Only through the integration of anthropology, odontology, molecular biology, radiocarbon dating, historical research, and genealogy were the remains able to be identified as Mr. Christopher Luke Moore, who drowned in 1928.Human identification is a process that necessarily requires a multidisciplinary and collaborative approach.

Virtual dental autopsy: undertaking forensic dental identification remotely using an intra-oral video camera.

INTRODUCTION: The aim of this study was to evaluate whether a forensic odontologist working remotely could accurately undertake forensic dental identifications using videos produced by non-dental forensic staff operating an intra-oral video camera (IOVC). The study's aims were to assess the accuracy and time taken to perform remote forensic dental identifications in this manner. MATERIALS AND METHODS: Eight cadavers from the Centre for Anatomy and Human Identification (CAHID), University of Dundee, UK, were examined by a forensic odontologist via a traditional dental examination. Their dental condition was recorded to serve as ante-mortem records for this study. Videos of each dentition were produced using an IOVC operated by a medical student. Post-mortem records were produced for each dentition from the videos by a remote second forensic odontologist who was not present at the traditional dental examination. The ante-mortem and post-mortem records were then compared, and identification was classified as positively established, possible or excluded. RESULTS: Established identifications were positively made in all eight cases although there were some non-critical inconsistencies between ante-mortem and post-mortem records. Before the second opinion, 85.6% of the teeth per study subject were charted consistently. After the second opinion, the percentage of consistency increased to 97.2%. Each video on average was about 4.13 minutes in duration and the average time taken to interpret and chart the post-mortem dental examination at the first attempt was 11.63 minutes. The time taken to chart from the videos was greater than is typical of a traditional dental examination. CONCLUSION: This pilot study supports the feasibility of undertaking remote dental identification. This novel "tele-dental virtopsy" approach could be a viable alternative to a traditional post-mortem dental examination, in situations where access to forensic dental services is difficult or limited due to geographical, logistical, safety, and/or political reasons.

Shotgun DNA sequencing for human identification: Dynamic SNP selection and likelihood ratio calculations accounting for errors.

Shotgun sequencing is a DNA analysis method that potentially determines the nucleotide sequence of every DNA fragment in a sample, unlike PCR-based genotyping methods that is widely used in forensic genetics and targets predefined short tandem repeats (STRs) or predefined single nucleotide polymorphisms (SNPs). Shotgun DNA sequencing is particularly useful for highly degraded low-quality DNA samples, such as ancient samples or those from crime scenes. Here, we developed a statistical model for human identification using shotgun sequencing data and developed formulas for calculating the evidential weight as a likelihood ratio (LR). The model uses a dynamic set of binary SNP loci and takes the error rate from shotgun sequencing into consideration in a probabilistic manner. To our knowledge, the method is the first to make this possible. Results from replicated shotgun sequencing of buccal swabs (high-quality samples) and hair samples (low-quality samples) were arranged in a genotype-call confusion matrix to estimate the calling error probability by maximum likelihood and Bayesian inference. Different genotype quality filters may be applied to account for genotyping errors. An error probability of zero resulted in the commonly used LR formula for the weight of evidence. Error probabilities above zero reduced the LR contribution of matching genotypes and increased the LR in the case of a mismatch between the genotypes of the trace and the person of interest. In the latter scenario, the LR increased from zero (occurring when the error probability was zero) to low positive values, which allow for the possibility that the mismatch may be due to genotyping errors. We developed an open-source R package, wgsLR, which implements the method, including estimation of the calling error probability and calculation of LR values. The R package includes all formulas used in this paper and the functionalities to generate the formulas.

Digital Dental Biometrics for Human Identification Based on Automated 3D Point Cloud Feature Extraction and Registration.

BACKGROUND: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. METHODS: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. RESULTS: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. CONCLUSIONS: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss.

Facial soft tissue depth of a contemporary adult Greek population.

Facial approximation is a technique that involves constructing the facial muscles and applying a suitable facial soft tissue depth (FSTD) dataset. To date, several FSTD studies have been conducted for varying population groups. This study aims to establish a FSTD dataset of an adult Greek population sample for the first time. The facial depths of subjects were measured on 100 head CT scans of 50 male and 50 female subjects aged from 18 to 99. The 3D head and skull models of subjects were segmented in Amira 6.1 by using histogram method. FSTDs were measured at 22 cranial landmarks (5 mid-sagittal, 17 bilateral). The FSTD dataset was generated by considering the age and sex of subjects. The impact of age and sex on the FSTD was limited. Slight inter-population depth variations were reported. Facial asymmetry calculated between the bilateral landmarks was insignificant for both male and female subjects.

Automatic personal identification using a single CT image.

OBJECTIVES: Computer vision (CV) mimics human vision, enabling computers to automatically compare radiological images from recent examinations with a large image database for unique identification, crucial in emergency scenarios involving unknown patients or deceased individuals. This study aims to extend a CV-based personal identification method from orthopantomograms (OPGs) to computed tomography (CT) examinations using single CT slices. METHODS: The study analyzed 819 cranial computed tomography (CCT) examinations from 722 individuals, focusing on single CT slices from six anatomical regions to explore their potential for CV-based personal identification in 69 procedures. CV automatically identifies and describes interesting features in images, which can be recognized in a reference image and then designated as matching points. In this study, the number of matching points was used as an indicator for identification. RESULTS: Across six different regions, identification rates ranged from 41/69 (59%) to 69/69 (100%) across over 700 possible identities. Comparison of images from the same individual achieved higher matching points, averaging 6.32 ± 0.52% (100% represents the maximum possible matching points), while images of different individuals averaged 0.94 ± 0.15%. Reliable matching points are found in the teeth, maxilla, cervical spine, skull bones, and paranasal sinuses, with the maxillary sinuses and ethmoidal cells being particularly suitable for identification due to their abundant matching points. CONCLUSION: Unambiguous identification of individuals based on a single CT slice is achievable, with maxillary sinus CT slices showing the highest identification rates. However, metal artifacts, especially from dental prosthetics, and various head positions can hinder identification. CLINICAL RELEVANCE STATEMENT: Radiology possesses a multitude of reference images for a CV database, facilitating automated CV-based personal identification in emergency examinations or cases involving unknown deceased individuals. This enhances patient care and communication with relatives by granting access to medical history. KEY POINTS: Unknown individuals in radiology or forensics pose challenges, addressed through automatic CV-based identification methods. A single CT slice highlighting the maxillary sinuses is particularly effective for personal identification. Radiology plays a pivotal role in automated personal identification by leveraging its extensive image database.

How reliable is stature estimation by dental means? Systematic review and meta-analysis.

Dental measurements have been proposed as parameters for stature estimation for at least 85 years. The scientific literature on the topic, however, is controversial regarding the performance of the method. This systematic literature review of observational cross-sectional studies aimed to compile evidence to support decisions in the forensic practice regarding the use of dental measurements for stature estimation. Embase, LILACS, MedLine (via PubMed), SciELO, Scopus, Web of Science, DansEasy and Open Access Thesis and Dissertations (OATD) were searched. Data regarding the rate of correct stature classifications were extracted. A meta-analysis with a Random Intercept Logistic Regression model and a Logit Transformation was conducted. The search led to 10.803 entries, out of which 15 were considered eligible (n = 1486 individuals). The studies were published between 1990 and 2020 and were authored by South American (n = 7) and Asian (n = 8) research teams. Dental measurements were predominantly (93.34 %) performed on dental casts or via intraoral inspection. The overall rate of correct classifications based on stature was 68 %. Excluding outliers, the overall accuracy of the method decreased to 64 % (95 %CI: 54-73 %). Significant heterogeneity was detected (I² = 72.4 %, τ2 = 0.24, H = 1.91, p < 0.001). Egger's test (p = 0.94) and the funnel plot did not reveal publication bias. Dental measurements are not reliable for stature estimation in the forensic field.

Reliability of a forensic odontology method for age-at-death estimation in adults: A Mexican case study.

This study aimed to evaluate the reliability of an age estimation method based on the pulp/tooth area ratio by assessing intra- and inter-examiner agreement across five observers at different intervals. Using the same X-ray device and technical parameters, 96 digital periapical X-ray images of upper and lower canines were obtained from 28 deceased people in Central America, whose age at death ranged from 19 to 49 years. Excellent and good agreement of results were achieved, and there were no statistically significant differences. The R2 value for upper teeth (54.0%) was higher than the R2 value for lower teeth (45.7%). The highest intraclass correlation coefficient value was 0.995 (0.993-0.997) and the lowest 0.798 (0.545-0.895). Inter-examiner agreement was high with values of 0.975 (0.965-0.983) and 0.927 (0.879-0.955). This method is adequate for assessing age in missing and unidentified people, including victims of mass disasters.

Evaluation of Sexual Dimorphism Using Condylar and Coronoid Mandibular Parameters in Orthopantomograms: A Pilot Study.

Background Gender determination is critical to forensic science and medico-legal applications. Given that it is the most dimorphic bone in the skull and is frequently found intact, the mandibular bone may be extremely important in determining gender. Orthopantomograms (OPGs) are quite helpful in accurately estimating age and sex in this regard. It is a laborious task for forensics to determine the gender of victims of mass casualties, natural disasters, and severely dismembered bodies. The mandible, which is susceptible to development spurts, has a high degree of accuracy for determining sex. Aim  This study aims to evaluate the potential use of coronoid height and condylar height as reliable anatomical markers for determining gender. Materials and methods In this study, 100 samples were used as study samples, 50 of which were male and 50 of which were female, in the age group of 20-30 years. The OPGs were obtained using a Planmeca Promax Scara 3 Digital OPG Machine (Planmeca, Helsinki, Finland), with settings of 70 kVp, 8 mA for 0.9 seconds, ensuring a 1:1 ratio. The images were then transferred to Planmeca Romexis® Viewer Software, Version 6.0 (Planmeca Oy, Helsinki, Finland) for measurement recording. Results Descriptive statistical analysis was done for this study and discriminant analysis was also done to create a population-specific formula. Results showed that the standard mean error for males concerning condylar height was 2.3 and coronoid height was 0.7. The standard mean error for females by condylar height was 1.6 and coronoid height was 0.6. The p-value was significant for coronoid height in both males and females. The p-value was not clinically significant for condylar height in both males and females. Conclusion The study's findings indicate that a larger mandibular angle is advantageous for gender assessment and helps with gender dimorphism. Out of both the parameters evaluated, coronoid height has shown statistical significance in both males and females. Hence, the study concludes that the parameter, coronoid height can be utilized to assess the gender of an individual.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification.

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people's security and promote their well-being.